Process simulation is the usage of processing experiments, typically using a computer, as directed by mathematical models created to describe a process phenomena. Many simulation and analysis tools (for example, Pisces, Medici, Suprem3, Suprem4 and PdFab) have been developed to assist process integration and device development. These tools have not been as widely employed for integrated circuit manufacturing. Generally, these tools are developed primarily for research and development purposes and do not adequately address various difficulties that arise in the manufacturing environment.
Several characteristics are generally applicable to the manufacturing environment and distinguish the manufacturing environment from a process integration and device development environment. One characteristic of the manufacturing environment is that measurable aspects of processes are have a fundamentally statistical nature, rather than a deterministic nature. Process variations and measurement errors are inherent to manufacturing processes so that substantially all data measured in a manufacturing environment is statistical. Exact measurement values are generally not available for each device at each stage of a manufacturing process so that a single data point is insufficient to justify a decision relating to the process. For example, if it is known that application of input parameters A and B to a fabrication process to yield an output variable C, what is truly known is that input parameters A and B each have a statistical profile that, when combined in the fabrication process, yield a statistical profile C shown in FIG. 1. The most useful information available in a manufacturing environment is the form of the statistical profile which results from the process. Unfortunately, nearly all information that is utilized in the manufacturing environment and all input parameters to a fabrication tool are expressed in the form of single-valued parameters, rather than in statistical profiles.
While conventional simulation and analysis tools do not suitably address the statistical nature of manufacturing processes, these tools are also deficient in failing to take advantage of the extensive process variables, in-line measurements and Wafer Electrical Testing (WET) data measurements that are available. WET testing includes testing for various device electrical parameters including threshold voltage and drive current that are measured at a wafer level, before bonding. Conventional simulation and analysis tools designed for research and development generally presume that a fabrication process is not yet operational for actual manufacturing. Therefore, these conventional simulation and analysis tools are not sufficiently flexible for a manufacturing engineer to make optimizations of the process. Specifically, these conventional tools do not allow the manufacturing engineer to utilize the extensive statistical data that is available in a manufacturing process to optimize the simulation model. Furthermore, for simulation tools that use either an empirical approach or an analytical approach, the model fitting parameters do not have sufficient degrees of freedom to match the extensive data that are available from an actual manufacturing process.
Another characteristic of the manufacturing environment is that monitoring of manufacturing processes and improvement of these processes is a fine-tuning process. Each tuning step includes a measurement of small differences in process variables with these differences being attributable at least in part to statistical fluctuations and also to complicated interactions between multiple reactions of the process as various process parameters are modified. Process results are typically difficult to measure with accuracy. A large number of highly variable factors influence process results. Modification of a single factor in isolation from other factors is difficult. This difficulty arises not only from a limited understanding of a factor's influence on the process but also because the various factors cause complex inter-related cross effects and interactions. Thus, a simulation and analysis tool that a production engineer confidently uses needs to supply a much higher order of measured precision of the data. Mere indications of data trends are insufficient. What is sought in the development of manufacturing tools and techniques is not a drastic change in a fabricated structure, but rather a small adjustment in characteristics. For example, what typically produces an improvement in an integrated circuit structure, such as an LDD structure in a transistor, is a change in dopant dosage of about ten percent or a change in applied temperature of 100.degree. C. The combination of the small size of the adjustments which are achieved by process modifications and the difficulty in measuring results of the modifications accurately manifest a disadvantageous characteristic of the manufacturing environment akin to a poor signal-to-noise ratio in signal processing.
Manufacturing simulation tools are calibrated prior to performing a simulation test. Calibration is typically accomplished by entering calibrated input parameters that are generated either experimentally or by previous simulation. In conventional manufacturing process calibration, a specified value of a parameter is fitted to produce a specified process output value. Realistic simulation results are rarely achieved using conventional simulation and calibration techniques since these techniques do not capture the true nature and complexity of the manufacturing process. Furthermore, conventional calibration processes require an intensive study of device engineers before a simulation tool becomes useful. Thus, the calibration processes cause significant delay in process qualification and improvement. This problem is worsened by the fact that calibration procedures are repeated continuously as the environment in the manufacturing area changes over time.
These characteristics of the manufacturing environment are applicable to an analysis of manufacturing monitoring as well as process improvements. For example, it is often desirable to know how processes change over time to track changes in fabrication results of a small amount, such as 3 percent, over time, for example 3 weeks to yield an ultimate result. Furthermore, these small differences are typically measured in an environment of statistical fluctuation and measurement error.
Although many characteristics of the manufacturing environment are disadvantageous, several are advantageous. One advantage is that specification of the structure resulting from the manufacturing process is well defined. Another advantage is that actual in-line measurement data acquired at various stages of the manufacturing process and actual WET data are plentiful and easily available. These data include statistical profile data that are highly informative regarding the manufacturing process.
What is sought is a technique for tracking and analyzing manufacturing processes such that inaccuracies arising from statistical fluctuations, complicated interactions, and measurement errors are avoided or compensated so that process modifications that produce even small differences can be measured, monitored and analyzed.